Civilian nuclear technology transfers as nonproliferation leverage: a reexamination of South Korea’s nuclear-weapons program
Bibliographic record
Abstract
This article draws on archival material, including recently declassified government documents, to examine the 1975–76 US effort to persuade South Korea to end its nuclear-weapons program. Contrary to earlier scholarship focused on the role of US security guarantees, the article finds that the single most important nonproliferation policy lever for the United States was its threat to withdraw its substantial civilian nuclear assistance, reinforced by the US enlistment of Canada to make a similar threat. This finding also challenges claims that civilian nuclear assistance creates a greater risk of weapons proliferation, and it suggests that—at least in some cases—such cooperation could in fact help further nonproliferation goals. The article additionally argues that the South Korean weapons program never went beyond its earliest stages and that it received far fewer resources than Seoul was capable of delivering. This raises the question of how much value Seoul placed on acquiring nuclear weapons in the first place; a broader question is whether the lessons of this case can be more generally applied to other nonproliferation cases. Finally, the article considers how further research can be directed at understanding the potentially synergistic elements of the relationship between civilian nuclear cooperation and nonproliferation policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".